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Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains

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  • O’Shaughnessy, Susan A.
  • Kim, Minyoung
  • Andrade, Manuel A.
  • Colaizzi, Paul D.
  • Evett, Steven R.

Abstract

Automated irrigation scheduling of grain crops using a combination of plant and soil water sensing feedback has not been widely investigated. A three-year study was conducted at Bushland, Texas to investigate irrigation management of grain sorghum (Sorghum bicolor, L.), in 2012 using plant feedback with a single thermal stress threshold, and in 2018 and 2019 using multiple thermal stress thresholds and a combination of plant and soil water sensing (Hybrid) feedback. The goals of the studies were to optimize grain yield, crop water productivity (CWP) and irrigation water productivity (IWP) using sensor feedback at irrigation levels similar to 80 %, 50 % and 30 % (designated I80, I50 and I30) replenishment of soil water depletion to field capacity as determined with weekly neutron probe readings (the “manual” method). Results in 2012 indicated that irrigation scheduling using plant feedback alone with a single thermal stress threshold produced grain yields that were significantly less (0.49 and 0.38 kg m−2) compared with the manual method (0.63 and 0.51 kg m−2) at the I80 and I50 treatment levels, respectively. However, in 2018, the Hybrid feedback method produced mean grain yields (0.87 kg m−2) that were significantly greater compared with the plant feedback (0.76 kg m−2) and manual (0.74 kg m−2) irrigation scheduling methods at the I80 treatment level. In 2019, mean grain yields (0.86, 0.83 and 0.88 kg m−2), CWP (1.25, 1.29 and 1.20 kg m-3) and IWP (2.11, 2.19 and 1.88 kg m-3) for the Hybrid, plant feedback and manual methods, respectively, were similar at the I80 level. These results suggest that plant and soil water sensing feedback using multiple thermal stress thresholds and watering levels have the potential to produce optimal crop response for grain sorghum. More research is required to test the efficacy of soil water sensing in combination with plant sensing for other crops.

Suggested Citation

  • O’Shaughnessy, Susan A. & Kim, Minyoung & Andrade, Manuel A. & Colaizzi, Paul D. & Evett, Steven R., 2020. "Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains," Agricultural Water Management, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:agiwat:v:240:y:2020:i:c:s0378377420302006
    DOI: 10.1016/j.agwat.2020.106273
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    References listed on IDEAS

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    2. O’Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D., 2015. "Dynamic prescription maps for site-specific variable rate irrigation of cotton," Agricultural Water Management, Elsevier, vol. 159(C), pages 123-138.
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    4. El-Naggar, A.G. & Hedley, C.B. & Horne, D. & Roudier, P. & Clothier, B.E., 2020. "Soil sensing technology improves application of irrigation water," Agricultural Water Management, Elsevier, vol. 228(C).
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    6. Li, Xiumei & Zhao, Weixia & Li, Jiusheng & Li, Yanfeng, 2019. "Maximizing water productivity of winter wheat by managing zones of variable rate irrigation at different deficit levels," Agricultural Water Management, Elsevier, vol. 216(C), pages 153-163.
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    1. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Fan, Yubing & Himanshu, Sushil K. & Ale, Srinivasulu & DeLaune, Paul B. & Zhang, Tian & Park, Seong C. & Colaizzi, Paul D. & Evett, Steven R. & Baumhardt, R. Louis, 2022. "The synergy between water conservation and economic profitability of adopting alternative irrigation systems for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 262(C).
    3. Himanshu, Sushil Kumar & Fan, Yubing & Ale, Srinivasulu & Bordovsky, James, 2021. "Simulated efficient growth-stage-based deficit irrigation strategies for maximizing cotton yield, crop water productivity and net returns," Agricultural Water Management, Elsevier, vol. 250(C).
    4. Katimbo, Abia & Rudnick, Daran R. & Liang, Wei-zhen & DeJonge, Kendall C. & Lo, Tsz Him & Franz, Trenton E. & Ge, Yufeng & Qiao, Xin & Kabenge, Isa & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions," Agricultural Water Management, Elsevier, vol. 274(C).
    5. McCarthy, Alison & Foley, Joseph & Raedts, Pieter & Hills, James, 2023. "Field evaluation of automated site-specific irrigation for cotton and perennial ryegrass using soil-water sensors and Model Predictive Control," Agricultural Water Management, Elsevier, vol. 277(C).
    6. Souza, Silas Alves & Rodrigues, Lineu Neiva, 2022. "Increased profitability and energy savings potential with the use of precision irrigation," Agricultural Water Management, Elsevier, vol. 270(C).

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